Fish measurement station keeping
A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.
1. A computer-implemented method comprising:
receiving information indicative of a particular type of fish activity, selected from among multiple types of fish activity, for which pictures are to be obtained;
providing information indicative of the particular type of fish activity, selected from among the multiple types of fish activity, to a machine-learning model that is trained to output, for a given type of fish activity and a set of one or more input parameters, a set of output parameters for obtaining pictures of fish engaging in the given fish activity within a fish pen;
obtaining, from the model, a particular set of output parameters; and
configuring one or more underwater cameras based on the particular set of output parameters.
2. The method of claim 1 , wherein the input parameters include a fish type, a location within the fish pen, a water current within the fish pen, or a light level within the fish pen.
3. The method of claim 1 , wherein the particular set of output parameters identifies a subset of the underwater cameras that are to be used to generate the images of the fish.
4. The method of claim 1 , wherein the particular set of output parameters identifies one or more camera angles associated with the underwater cameras that are to be used to generate the images of the fish.
5. The method of claim 1 , wherein the output parameters identifies one or more of location, area topology, timing, lighting, depth, salinity, current, or temperature, associated with the particular type of fish activity within the fish pen.
6. The method of claim 1 , wherein the particular set of output of output parameters specifies a particular mode of the one or more underwater cameras, selected from among multiple modes that are pre-associated with the one or more underwater cameras.
7. The method of claim 1 , wherein configuring the one or more underwater cameras comprises adjusting a location or an orientation of one or more of the underwater cameras within the fish pen.
8. The method of claim 1 , wherein the multiple types of fish activity include sleeping, feeding, swimming separately from a school, or moving according to a particular movement pattern.
9. The method of claim 1 , wherein the information indicative of the particular type of fish activity is selected from among the multiple types of fish activity via a user-input.
10. A system comprising:
one or more computing devices and one or more storage devices that store instructions which, when executed by the one or more computing devices, cause the one or more computing devices to perform operations comprising:
receiving information indicative of a particular type of fish activity, selected from among multiple types of fish activity, for which pictures are to be obtained;
providing information indicative of a of the particular type of fish activity, selected from among the multiple types of fish activity, to a machine-learning model that is trained to output, for a given type of fish activity and a set of one or more input parameters, a set of output parameters for obtaining pictures of fish engaging in the given fish activity within a fish pen;
obtaining, from the model, a particular set of output parameters; and
configuring one or more underwater cameras based on the particular set of output parameters.
11. The system of claim 10 , wherein the input parameters include a fish type, a location within the fish pen, a water current within the fish pen, or a light level within the fish pen.
12. The system of claim 10 , wherein the particular set of output parameters identifies a subset of the underwater cameras that are to be used to generate the images of the fish.
13. The system of claim 10 , wherein the particular set of output parameters identifies one or more camera angles associated with the underwater cameras that are to be used to generate the images of the fish.
14. The system of claim 10 , wherein the output parameters identifies one or more of location, area topology, timing, lighting, depth, salinity, current, or temperature, associated with the particular type of fish activity within the fish pen.
15. The system of claim 10 , wherein the particular set of output of output parameters specifies a particular mode of the one or more underwater cameras, selected from among multiple modes that are pre-associated with the one or more underwater cameras.
16. The system of claim 10 , wherein configuring the one or more underwater cameras comprises adjusting a location or an orientation of one or more of the underwater cameras within the fish pen.
17. The system of claim 10 , wherein the multiple types of fish activity include sleeping, feeding, swimming separately from a school, or moving according to a particular movement pattern.
18. The system of claim 10 , wherein the information indicative of the particular type of fish activity is selected from among the multiple types of fish activity via a user-input.
19. One or more non-transitory computer-readable storage media comprising instructions, which, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising:
receiving information indicative of a particular type of fish activity, selected from among multiple types of fish activity, for which pictures are to be obtained;
providing information indicative of the particular type of fish activity, selected from among the multiple types of fish activity, to a machine-learning model that is trained to output, for a given type of fish activity and a set of one or more input parameters, a set of output parameters for obtaining pictures of fish engaging in the given fish activity within a fish pen;
obtaining, from the model, a particular set of output parameters; and
configuring one or more underwater cameras based on the particular set of output parameters.
20. The storage media of claim 19 , wherein configuring the one or more underwater cameras comprises adjusting a location or an orientation of one or more of the underwater cameras within the fish pen.